# Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Rail Defect Detection

arXiv: https://arxiv.org/abs/2401.00393 · HTML: https://mlsanity.com/paper/2401.00393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Segmentation-Based Deep-Learning Approach for Surface-Defect Detection](https://mlsanity.com/paper/1903.08536/) (2019)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection](https://mlsanity.com/paper/2103.15158/) (2021)
- [Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems](https://mlsanity.com/paper/2012.03448/) (2020)
- [Image Data Augmentation Approaches: A Comprehensive Survey and Future directions](https://mlsanity.com/paper/2301.02830/) (2023)
- [Neuro-SERKET: Development of Integrative Cognitive System through the Composition of Deep Probabilistic Generative Models](https://mlsanity.com/paper/1910.08918/) (2019)
- [Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation](https://mlsanity.com/paper/2302.08366/) (2023)
- [Use of Neural Signals to Evaluate the Quality of Generative Adversarial Network Performance in Facial Image Generation](https://mlsanity.com/paper/1811.04172/) (2018)
- [A Probe Towards Understanding GAN and VAE Models](https://mlsanity.com/paper/1812.05676/) (2018)
- [Leveraging Generative AI Models for Synthetic Data Generation in Healthcare: Balancing Research and Privacy](https://mlsanity.com/paper/2305.05247/) (2023)
- [Improving VAE based molecular representations for compound property prediction](https://mlsanity.com/paper/2201.04929/) (2022)

## Built on by (most cited first, 1 of 1)

- [DefectTwin: When LLM Meets Digital Twin for Railway Defect Inspection](https://mlsanity.com/paper/2409.06725/) (2024)

## Abstract (arXiv)

Recent advancements in cognitive computing, with the integration of deep learning techniques, have facilitated the development of intelligent cognitive systems (ICS). This is particularly beneficial in the context of rail defect detection, where the ICS would emulate human-like analysis of image data for defect patterns. Despite the success of Convolutional Neural Networks (CNN) in visual defect classification, the scarcity of large datasets for rail defect detection remains a challenge due to infrequent accident events that would result in defective parts and images. Contemporary researchers have addressed this data scarcity challenge by exploring rule-based and generative data augmentation models. Among these, Variational Autoencoder (VAE) models can generate realistic data without extensive baseline datasets for noise modeling. This study proposes a VAE-based synthetic image generation technique for rail defects, incorporating weight decay regularization and image reconstruction loss to prevent overfitting. The proposed method is applied to create a synthetic dataset for the Canadian Pacific Railway (CPR) with just 50 real samples across five classes. Remarkably, 500 synthetic samples are generated with a minimal reconstruction loss of 0.021. A Visual Transformer (ViT) model underwent fine-tuning using this synthetic CPR dataset, achieving high accuracy rates (98%-99%) in classifying the five defect classes. This research offers a promising solution to the data scarcity challenge in rail defect detection, showcasing the potential for robust ICS development in this domain.